摘要
针对电力负荷数据由于受多种因素的影响具有高度不确定性的问题,将负荷数据的不确定性与深度学习算法相结合,提出了一种基于模糊逻辑的FBiLSTM-Attention短期负荷预测模型,以提高负荷预测的精度。首先,对原始数据进行数据预处理,包括缺失值填充、相关性分析及数据归一化;其次,通过K-Means聚类将每个特征的数据转换成模糊规则引入模糊逻辑的处理,同时,模型结构方面采用双向长短期记忆网络(BiLSTM)和注意力机制(Attention);最后,对所提方法和传统的LSTM与BiLSTM-Attention模型的预测结果进行对比。结果表明,结合了模糊逻辑的模型精确度和鲁棒性都有了明显的提升,具有更好的预测性能。所提模型可以有效提高处理不确定性数据的能力,为负荷预测研究提供了参考。
Aiming at the problem of high uncertainty in power load data due to various factors,a fuzzy logic based FBiLSTM Attention short-term load forecasting model was proposed by combining the uncertainty of load data with deep learning algorithms to improve the accuracy of load forecasting.Firstly,the raw data,including filling in missing values,conducting correlation analysis and normalizing the data,was preprocessed.Secondly,K-Means clustering was used to transform the data of each feature into fuzzy rules and introduce fuzzy logic processing.In terms of model structure,a bi-directional long short-term memory(BiLSTM)and attention mechanism(Attention)were adopted.Finally,the prediction results of the proposed method with traditional LSTM and BiLSTM Attention models were compared.The results show that the model combined with fuzzy logic has significantly improved accuracy and robustness,and has better predictive performance.The proposed model can effectively improve the ability to handle uncertain data,providing reference for load forecasting study.
作者
张岩
康泽鹏
高晓芝
杨楠
王昭雷
ZHANG Yan;KANG Zepeng;GAO Xiaozhi;YANG Nan;WANG Zhaolei(School of Electrical Engineering,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China;School of Electrical Engineering and New Energy,Three Gorges University,Yichang,Hubei 443002,China;Ultra High Voltage Branch,State Grid Hebei Electric Power Company Limited,Shijiazhuang,Hebei 050070,China)
出处
《河北科技大学学报》
北大核心
2025年第1期41-48,共8页
Journal of Hebei University of Science and Technology
基金
国家自然科学基金(62233006)
河北省高等学校科学技术研究项目(ZD2021202,QN2022028)。
关键词
数据处理
模糊逻辑
负荷预测
双向长短期记忆网络
注意力机制
data processing
fuzzy logic
load forecasting
bi-directional long short-term memory(BiLSTM)
attention mechanism(Attention)
